Regression Models and Utilities for Repeated Measures and Panel Data
Check if variables are constant or variable over time.
Estimate asymmetric effects models using first differences
Asymmetric effects models fit with GEE
Filter out entities with too few observations
Estimate first differences models using GLS
Tidy methods for fdm and asym models
Retrieve model formulas from wbm objects
Retrieve panel_data metadata
Estimate Heise stability and reliability coefficients
Check if object is panel_data
Plot trends in longitudinal variables
Convert wide panels to long format
Generate differenced and asymmetric effects data
Prepare data for within-between modeling
Make model frames for panel_data objects
Number of observations used in wbm models
Create panel data frames
Predictions and simulations from within-between GEE models
Predictions and simulations from within-between models
Objects exported from other packages
Summarize panel data frames
Convert panel_data to regular data frame
Panel regression models fit with GEE
Tidy methods for wbgee models
Within-Between Model (wbm) class
Panel regression models fit via multilevel modeling
Bayesian estimation of within-between models
Tidy methods for wbm models
Convert long panel data to wide format
Provides an object type and associated tools for storing and wrangling panel data. Implements several methods for creating regression models that take advantage of the unique aspects of panel data. Among other capabilities, automates the "within-between" (also known as "between-within" and "hybrid") panel regression specification that combines the desirable aspects of both fixed effects and random effects econometric models and fits them as multilevel models (Allison, 2009 <doi:10.4135/9781412993869.d33>; Bell & Jones, 2015 <doi:10.1017/psrm.2014.7>). These models can also be estimated via generalized estimating equations (GEE; McNeish, 2019 <doi:10.1080/00273171.2019.1602504>) and Bayesian estimation is (optionally) supported via 'Stan'. Supports estimation of asymmetric effects models via first differences (Allison, 2019 <doi:10.1177/2378023119826441>) as well as a generalized linear model extension thereof using GEE.